Ethics of AI in the Workplace

As Artificial Intelligence (AI) adoption at work continues to rise, so do concerns about how AI will transform the workplace and society at large. From fears that AI will eliminate or replace millions of jobs to the potential environmental impacts of generative AI data centers, this emerging technology continues to spark discussion about the ethics of AI and its growing presence in our lives.

While this topic is constantly evolving and new developments rapidly shift the conversation, here are some of the most pressing ethical issues facing AI in the workplace in 2026, and how they might be addressed by future innovations, policies, and individuals.

AI Ethics Concern: Job Displacement

Can AI really replace your job? For some industries, a future transformed by automation seems likely—projections by the McKinsey Global Institute estimate nearly 30% of hours worked today could be automated by 2030. And AI may already be contributing to reductions in entry-level hiring across multiple industries. As routine tasks and even entire roles can be offloaded to AI models, the nature of work itself may shift as corporate adoption of AI tools grows.

However, like other technological advancements of the past, the McKinsey research suggests that post-automation employment levels are likely to remain stable as roles emerge to replace those automated or transformed by AI.

Solutions and Considerations

• Organizations can train and upskill existing employees in AI tools and augment job descriptions to incorporate the use of AI. Some companies are already doing this; the Workmonitor 2026 report by Randstad found that in 2025, job postings requiring “AI Agent” skills increased by 1,587%.
• Tasks or roles that involve the EPOCH framework of human capabilities can drive growth across organizations in a post-AI workplace. EPOCH, an acronym for Empathy, Presence, Opinion, Creativity, and Hope, highlights aspects of human thinking and reasoning that machines have difficulty replicating.

Concern: Privacy & Cybersecurity

Companies are already increasingly targeted by cybercriminals, and enterprise-level AI systems can leave an organization more vulnerable to threats. In addition to AI-assisted cyberattacks, cybercriminals may attempt to extract sensitive proprietary data from AI systems, poison training data, or manipulate systems in ways that alter their behavior. Furthermore, AI can also contribute to breaches of privacy—whether through extensive AI-enabled employee monitoring software, or a security breach of a proprietary AI system itself, resulting in sensitive employee or customer data falling into the wrong hands.

Solutions and Considerations

• Companies can use AI-enabled cybersecurity solutions to help detect and respond to AI-assisted attacks.
• Organizations using AI tools can perform regular audits of their systems and remove unnecessary sensitive data to reduce the likelihood and impact of breaches.
• Address employee privacy concerns with transparent policies around AI-enabled employee monitoring. Employees should know how data is collected, what information is stored, and how it is used.

Concern: Accountability

Agentic AI is a system of machine learning “agents” that mimic human decision-making to solve problems in real time. While still evolving, agentic AI systems may eventually be able to perform autonomous, complex workflows without human intervention, such as handling customer service inquiries or placing supply chain orders after analyzing inventory levels.

As these models develop more sophisticated decision-making abilities, it raises the question of who is responsible when something goes wrong. And things can and do go wrong—agentic AI can make poor decisions due to incomplete data, conflicting parameters, or inadequate safeguards. Depending on the task, mistakes can result in financial losses, operational disruption, or the spread of harm or misinformation.

Solutions and Considerations:

• Agentic AI deployments should include transparency around how decisions are made.
• Companies using AI agents should build safeguards and maintain human oversight for crucial decisions, with policies defining responsibility and accountability.

Concern: Bias & Discrimination

All AI systems are trained on vast amounts of human-created data. The more data the AI receives, the better it can perform its intended function. However, humans are subject to both implicit and explicit biases. These biases may be learned and reproduced by AI systems, leading to potential harm or even discrimination.

For example, if a Human Resources team uses AI to screen resumes and the system is trained on historical hiring data reflecting demographic bias, the AI may unintentionally perpetuate that pattern, rejecting qualified candidates from underrepresented groups.

Solutions and Considerations

• Companies should regularly test and audit AI models to avoid biased outcomes and skewed training data.
• Ensure training databases are representative and diverse.
• Involve humans as final oversight to review decisions and catch potential issues.

Concern: Accuracy

AI hallucination is a phenomenon in which an AI system, typically a Large Language Model (LLM), generates a convincing response that is factually incorrect, misleading, or unsupported by reliable sources. LLMs rely on pattern recognition and probabilities, and when exposed to training data that is too broad, vague, outdated, or contradictory, a hallucination is likely to occur. Without proper oversight, relying on inaccurate AI output can have serious consequences. While AI accuracy continues to improve, AI hallucination and inaccuracies are still a problem—a late 2025 study by the BBC and European Broadcasting Union found that AI assistants misrepresented news content 45% of the time.

Solutions and Considerations

• Proprietary AI systems should have guardrails that adhere to company standards to reduce hallucinations.
• Encourage systems and workflows that allow users to verify information using reliable sources.

Concern: Cognitive Offloading & Skill Atrophy

AI tools can boost productivity and efficiency by automating tasks such as data analysis, transcription, generating code, and drafting communications. However, heavy reliance on AI may come with an unintended consequence—skill atrophy. Like how relying on GPS can weaken navigational skills, using AI tools is a form of cognitive offloading that may contribute to atrophy of critical thinking, reasoning, and problem-solving skills. This area is still being studied, but early research—like this MIT study indicating participants who used an LLM to write essays demonstrated reduced ownership and recall ability of their work—has worrying implications for the future of many industries as AI usage becomes more mainstream.

Solutions and Considerations

• AI should be used as a tool to support human work, not to fully replace human judgment or skill development. Don’t let its use come at the expense of reduced confidence in one’s own knowledge, creativity, or decision-making abilities.
• Individuals should evaluate when the use of AI is helpful or a potential hindrance.

Ethics and the Future of AI

AI is already transforming workplaces and industries worldwide. The speed at which AI is developing means the nature of work will likely look completely different in 5-10 years, or even next year. Ongoing discussions about AI ethics will remain important as developers, organizations, and employees adapt to this powerful technology and its application in the workplace.